Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.
Explores Markov chains, their properties, and algorithmic applications, emphasizing information quantification and state monotonicity.
Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.
Explores the canonical basis in linear algebra, focusing on matrix representation, diagonalizability, and characteristic polynomials.
Covers the reduction of a linear transformation in a 2-dimensional space to find a simpler matrix representation.
Delves into the proof of the convergence rate theorem for an ergodic Markov chain, emphasizing eigenvalues and detailed balance properties.
